





import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor





class 		_A:


				"""simple docstring"""


				def __init__(     self	,   _A	,   _A=13	,   _A=30	,   _A=2	,   _A=3	,   _A=True	,   _A=True	,   _A=32	,   _A=5	,   _A=4	,   _A=37	,   _A="gelu"	,   _A=0.1	,   _A=0.1	,   _A=10	,   _A=0.0_2	,   _A=None	,   _A=2	,   ):
									__A      :		str         =							parent
									__A      :		int         =							batch_size
									__A      :		Optional[Any]         =							image_size
									__A      :		str         =							patch_size
									__A      :		str         =							num_channels
									__A      :		int         =							is_training
									__A      :		Optional[int]         =							use_labels
									__A      :		Optional[int]         =							hidden_size
									__A      :		int         =							num_hidden_layers
									__A      :		Dict         =							num_attention_heads
									__A      :		int         =							intermediate_size
									__A      :		List[str]         =							hidden_act
									__A      :		Any         =							hidden_dropout_prob
									__A      :		Tuple         =							attention_probs_dropout_prob
									__A      :		int         =							type_sequence_label_size
									__A      :		List[Any]         =							initializer_range
									__A      :		Optional[int]         =							scope
									__A      :		Tuple         =							encoder_stride

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									__A      :		int         =							(image_size // patch_size) ** 2
									__A      :		Optional[Any]         =							num_patches + 1



				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[Any]         =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

									__A      :		str         =							None
									if self.use_labels:
														__A      :		Union[str, Any]         =							ids_tensor([self.batch_size]	,   self.type_sequence_label_size	)

									__A      :		Tuple         =							self.get_config()

									return config, pixel_values, labels



				def    UpperCAmelCase_						(     self	):
									return ViTConfig(
									    image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   hidden_size=self.hidden_size	,   num_hidden_layers=self.num_hidden_layers	,   num_attention_heads=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   is_decoder=a_	,   initializer_range=self.initializer_range	,   encoder_stride=self.encoder_stride	,   )



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									__A      :		List[str]         =							ViTModel(config=a_	)
									model.to(a_	)
									model.eval()
									__A      :		Any         =							model(a_	)
									self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)	)



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									__A      :		Union[str, Any]         =							ViTForMaskedImageModeling(config=a_	)
									model.to(a_	)
									model.eval()
									__A      :		Union[str, Any]         =							model(a_	)
									self.parent.assertEqual(
									    result.reconstruction.shape	,   (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

									# test greyscale images
									__A      :		List[Any]         =							1
									__A      :		int         =							ViTForMaskedImageModeling(a_	)
									model.to(a_	)
									model.eval()

									__A      :		int         =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
									__A      :		Union[str, Any]         =							model(a_	)
									self.parent.assertEqual(result.reconstruction.shape	,   (self.batch_size, 1, self.image_size, self.image_size)	)



				def    UpperCAmelCase_						(     self	,   _A	,   _A	,   _A	):
									__A      :		Any         =							self.type_sequence_label_size
									__A      :		List[Any]         =							ViTForImageClassification(a_	)
									model.to(a_	)
									model.eval()
									__A      :		str         =							model(a_	,   labels=a_	)
									self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)	)

									# test greyscale images
									__A      :		List[str]         =							1
									__A      :		str         =							ViTForImageClassification(a_	)
									model.to(a_	)
									model.eval()

									__A      :		int         =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
									__A      :		str         =							model(a_	)
									self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)	)





				def    UpperCAmelCase_						(     self	):
									__A      :		Tuple         =							self.prepare_config_and_inputs()
									(
									    __A		
									)      :		str         =							config_and_inputs
									__A      :		Dict         =							{"pixel_values": pixel_values}
									return config, inputs_dict





@require_torch
class 		_A(   _A							,					_A							,					unittest.TestCase  ):


				"""simple docstring"""


				UpperCamelCase       :    Optional[int]          =      (
				    (
				        ViTModel,
				        ViTForImageClassification,
				        ViTForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase       :    Optional[int]          =      (
				    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase       :    Tuple          =      True

				UpperCamelCase       :    Any          =      False
				UpperCamelCase       :    Dict          =      False
				UpperCamelCase       :    Union[str, Any]          =      False



				def    UpperCAmelCase_						(     self	):
									__A      :		str         =							ViTModelTester(self	)
									__A      :		Dict         =							ConfigTester(self	,   config_class=a_	,   has_text_modality=a_	,   hidden_size=37	)



				def    UpperCAmelCase_						(     self	):
									self.config_tester.run_common_tests()



				@unittest.skip(reason='ViT does not use inputs_embeds'	)
				def    UpperCAmelCase_						(     self	):
									pass



				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														__A      :		Any         =							model_class(a_	)
														self.assertIsInstance(model.get_input_embeddings()	,   (nn.Module)	)
														__A      :		Optional[Any]         =							model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(a_	,   nn.Linear	)	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Union[str, Any]         =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														__A      :		List[str]         =							model_class(a_	)
														__A      :		int         =							inspect.signature(model.forward	)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														__A      :		Any         =							[*signature.parameters.keys()]

														__A      :		List[str]         =							["pixel_values"]
														self.assertListEqual(arg_names[:1]	,   a_	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Any         =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Any         =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*a_	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Tuple         =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a_	)





				@slow
				def    UpperCAmelCase_						(     self	):
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__A      :		Union[str, Any]         =							ViTModel.from_pretrained(a_	)
														self.assertIsNotNone(a_	)







def 					_SCREAMING_SNAKE_CASE							(  ) -> Optional[int]:
					__A      :		Optional[int]         =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
					return image





@require_torch
@require_vision
class 		_A(   unittest.TestCase  ):


				"""simple docstring"""


				@cached_property
				def    UpperCAmelCase_						(     self	):
									return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'	) if is_vision_available() else None



				@slow
				def    UpperCAmelCase_						(     self	):
									__A      :		int         =							ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'	).to(a_	)

									__A      :		Tuple         =							self.default_image_processor
									__A      :		List[str]         =							prepare_img()
									__A      :		List[str]         =							image_processor(images=a_	,   return_tensors='pt'	).to(a_	)

									# forward pass
									with torch.no_grad():
														__A      :		int         =							model(**a_	)

									# verify the logits
									__A      :		List[str]         =							torch.Size((1, 1000)	)
									self.assertEqual(outputs.logits.shape	,   a_	)

									__A      :		Any         =							torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]	).to(a_	)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,   a_	,   atol=1e-4	)	)



				@slow
				def    UpperCAmelCase_						(     self	):
									# ViT models have an `interpolate_pos_encoding` argument in their forward method,
									# allowing to interpolate the pre-trained position embeddings in order to use
									# the model on higher resolutions. The DINO model by Facebook AI leverages this
									# to visualize self-attention on higher resolution images.
									__A      :		Optional[Any]         =							ViTModel.from_pretrained('facebook/dino-vits8'	).to(a_	)

									__A      :		int         =							ViTImageProcessor.from_pretrained('facebook/dino-vits8'	,   size=480	)
									__A      :		Union[str, Any]         =							prepare_img()
									__A      :		List[Any]         =							image_processor(images=a_	,   return_tensors='pt'	)
									__A      :		List[Any]         =							inputs.pixel_values.to(a_	)

									# forward pass
									with torch.no_grad():
														__A      :		Tuple         =							model(a_	,   interpolate_pos_encoding=a_	)

									# verify the logits
									__A      :		Optional[int]         =							torch.Size((1, 3601, 384)	)
									self.assertEqual(outputs.last_hidden_state.shape	,   a_	)

									__A      :		List[Any]         =							torch.tensor(
									    [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]	).to(a_	)

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,   a_	,   atol=1e-4	)	)





				@slow
				@require_accelerate
				@require_torch_gpu
				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							ViTModel.from_pretrained('facebook/dino-vits8'	,   torch_dtype=torch.floataa	,   device_map='auto'	)
									__A      :		Union[str, Any]         =							self.default_image_processor

									__A      :		Tuple         =							prepare_img()
									__A      :		Any         =							image_processor(images=a_	,   return_tensors='pt'	)
									__A      :		Optional[Any]         =							inputs.pixel_values.to(a_	)

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
														__A      :		Dict         =							model(a_	)











'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



